Alberta Infant Motor Scale: Reliability and Validity When Used on Preterm Infants in Taiwan
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The goal of this study was to examine the reliability and validity of measurements obtained with the Alberta Infant Motor Scale (AIMS) for evaluation of preterm infants in Taiwan. SUBJECTS: Two independent groups of preterm infants were used to investigate the reliability (n=45) and validity (n=41) for the AIMS. METHODS: In the reliability study, the AIMS was administered to the infants by a physical therapist, and infant performance was videotaped. The performance was then rescored by the same therapist and by 2 other therapists to examine the intrarater and interrater reliability. In the validity study, the AIMS and the Bayley Motor Scale were administered to the infants at 6 and 12 months of age to examine criterion-related validity. RESULTS: Intraclass correlation coefficients (ICCs) for intrarater and interrater reliability of measurements obtained with the AIMS were high (ICC=.97-.99). The AIMS scores correlated with the Bayley Motor Scale scores at 6 and 12 months (r=.78 and.90), although the AIMS scores at 6 months were only moderately predictive of the motor function at 12 months (r=.56). CONCLUSION AND DISCUSSION: The results suggest that measurements obtained with the AIMS have acceptable reliability and concurrent validity but limited predictive value for evaluating preterm Taiwanese infants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".